def main(args):
chunks = latest_chunks()
parser, next_batch = chunks2batches(chunks)
tfprocess = TFProcess(next_batch)
if args:
restore_file = args.pop(0)
print("Restoring weights ....")
tfprocess.restore(restore_file)
print("Training starts ....")
while True:
change_data, run_val = tfprocess.process()
if change_data:
chunks = latest_chunks()
parser.chunk_switch(chunks)
if run_val:
best_net = leela_conf.SAVE_DIR + "/best.txt"
last_net = leela_conf.SAVE_DIR + "/latest.txt"
cmd = leela_conf.VALIDATION_COMMAND % \
(last_net,
best_net)
print(cmd)
subprocess.call(cmd.split(" ")) #, stdout=subprocess.PIPE)
with open(leela_conf.VALIDATION_LOG, "r") as f:
better = int(f.readlines()[-1].split("\t")[0])
if better:
print("---------------- Better Network Found! --------------")
copy2(last_net, best_net)
else:
print("------------- Checkout best net so far. -------------")
tfprocess.replace_weights(get_weights(best_net))
name='x')
probs = tf.placeholder(tf.float32, [None, BOARD_SIZE * BOARD_SIZE + 1])
winner = tf.placeholder(tf.float32, [None, 1])
else:
planes = tf.placeholder(tf.float32,
[None, FEATURES, BOARD_SIZE, BOARD_SIZE],
name='x')
probs = tf.placeholder(tf.float32, [None, BOARD_SIZE * BOARD_SIZE + 1])
winner = tf.placeholder(tf.float32, [None, 1])
tfprocess = TFProcess()
tfprocess.TFCOREML = True
tfprocess.DATA_FORMAT = data_format
tfprocess.BOARD_SIZE = BOARD_SIZE
tfprocess.INPUT_DIM = 2
tfprocess.FEATURES = FEATURES
tfprocess.RESIDUAL_FILTERS = filters
tfprocess.RESIDUAL_BLOCKS = blocks
if BOARD_SIZE == 9:
tfprocess.VALUE_FULLY_CONNECTED = 64
tfprocess.training = False # batch normalizationをコンバートするため
tfprocess.init_net(planes, probs, winner)
tfprocess.replace_weights(weights)
tf.train.write_graph(tf.get_default_graph(),
os.path.dirname(sys.argv[3]),
os.path.basename(sys.argv[3]),
as_text=True)
with tf.get_default_graph().as_default():
saver = tf.train.Saver()
print(saver.save(tfprocess.session, "./tmp/model.ckpt"))
weights = []
for e, line in enumerate(f):
if e == 0:
#Version
print("Version", line.strip())
if line != '1\n':
raise ValueError("Unknown version {}".format(line.strip()))
else:
weights.append(list(map(float, line.split(' '))))
if e == 2:
channels = len(line.split(' '))
print("Channels", channels)
blocks = e - (4 + 14)
if blocks % 8 != 0:
raise ValueError("Inconsistent number of weights in the file")
blocks //= 8
print("Blocks", blocks)
tfprocess = TFProcess()
tfprocess.init(batch_size=1)
if tfprocess.RESIDUAL_BLOCKS != blocks:
raise ValueError("Number of blocks in tensorflow model doesn't match "\
"number of blocks in input network")
if tfprocess.RESIDUAL_FILTERS != channels:
raise ValueError("Number of filters in tensorflow model doesn't match "\
"number of filters in input network")
tfprocess.replace_weights(weights)
path = os.path.join(os.getcwd(), "leelaz-model")
save_path = tfprocess.saver.save(tfprocess.session, path, global_step=0)
print("Version", line.strip())
if line != '1\n':
raise ValueError("Unknown version {}".format(line.strip()))
else:
weights.append(list(map(float, line.split(' '))))
if e == 2:
channels = len(line.split(' '))
print("Channels", channels)
blocks = e - (4 + 14)
if blocks % 8 != 0:
raise ValueError("Inconsistent number of weights in the file")
blocks /= 8
print("Blocks", blocks)
return weights
if __name__ == '__main__':
gpu_num = 2
x = [[
tf.placeholder(tf.float32, [None, 18, 19 * 19]),
tf.placeholder(tf.float32, [None, 362]),
tf.placeholder(tf.float32, [None, 1])
] for _ in range(gpu_num)]
tfprocess = TFProcess(x)
tfprocess.save_leelaz_weights("./save/random.txt")
path = os.path.join(leela_conf.SAVE_DIR, "leelaz-model")
tfprocess.replace_weights(get_weights(sys.argv[1]))
print("saved to: ", path)
save_path = tfprocess.save(0, path)
